Overview

Dataset statistics

Number of variables71
Number of observations11430
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 MiB
Average record size in memory568.0 B

Variable types

Categorical41
Numeric30

Alerts

ratio_nullHyperlinks has constant value "0" Constant
ratio_intRedirection has constant value "0" Constant
ratio_intErrors has constant value "0" Constant
submit_email has constant value "0" Constant
sfh has constant value "0" Constant
special_characters has constant value "0" Constant
url has a high cardinality: 11429 distinct values High cardinality
ip is highly correlated with tld_in_subdomain and 1 other fieldsHigh correlation
nb_www is highly correlated with nb_subdomains and 1 other fieldsHigh correlation
length_words_raw is highly correlated with ip and 8 other fieldsHigh correlation
char_repeat is highly correlated with length_words_raw and 4 other fieldsHigh correlation
shortest_words_raw is highly correlated with shortest_word_host and 1 other fieldsHigh correlation
shortest_word_host is highly correlated with nb_www and 3 other fieldsHigh correlation
shortest_word_path is highly correlated with avg_word_pathHigh correlation
longest_words_raw is highly correlated with char_repeat and 5 other fieldsHigh correlation
longest_word_host is highly correlated with shortest_word_host and 1 other fieldsHigh correlation
longest_word_path is highly correlated with char_repeat and 5 other fieldsHigh correlation
avg_words_raw is highly correlated with longest_words_raw and 4 other fieldsHigh correlation
avg_word_host is highly correlated with shortest_words_raw and 2 other fieldsHigh correlation
avg_word_path is highly correlated with longest_words_raw and 3 other fieldsHigh correlation
nb_hyperlinks is highly correlated with empty_title and 1 other fieldsHigh correlation
ratio_intHyperlinks is highly correlated with ratio_extHyperlinks and 6 other fieldsHigh correlation
ratio_extHyperlinks is highly correlated with ratio_intHyperlinks and 5 other fieldsHigh correlation
nb_extCSS is highly correlated with external_faviconHigh correlation
external_favicon is highly correlated with ratio_intHyperlinks and 2 other fieldsHigh correlation
links_in_tags is highly correlated with ratio_intHyperlinks and 4 other fieldsHigh correlation
ratio_intMedia is highly correlated with ratio_intHyperlinks and 3 other fieldsHigh correlation
ratio_extMedia is highly correlated with ratio_intHyperlinks and 2 other fieldsHigh correlation
empty_title is highly correlated with ratio_intHyperlinks and 2 other fieldsHigh correlation
domain_age is highly correlated with domain_in_brand and 1 other fieldsHigh correlation
google_index is highly correlated with page_rank and 1 other fieldsHigh correlation
page_rank is highly correlated with domain_in_brand and 3 other fieldsHigh correlation
status is highly correlated with safe_anchor and 3 other fieldsHigh correlation
url_length is highly correlated with nb_com and 9 other fieldsHigh correlation
digits is highly correlated with http_in_path and 6 other fieldsHigh correlation
nb_dslash is highly correlated with sfh and 6 other fieldsHigh correlation
http_in_path is highly correlated with nb_com and 4 other fieldsHigh correlation
domain_in_brand is highly correlated with domain_age and 1 other fieldsHigh correlation
safe_anchor is highly correlated with ratio_intHyperlinks and 3 other fieldsHigh correlation
port is highly correlated with sfh and 5 other fieldsHigh correlation
sfh is highly correlated with port and 38 other fieldsHigh correlation
brand_in_path is highly correlated with length_words_rawHigh correlation
ratio_nullHyperlinks is highly correlated with port and 38 other fieldsHigh correlation
shortening_service is highly correlated with sfh and 5 other fieldsHigh correlation
suspecious_tld is highly correlated with sfh and 5 other fieldsHigh correlation
dns_record is highly correlated with abnormal_subdomain and 1 other fieldsHigh correlation
random_domain is highly correlated with sfh and 5 other fieldsHigh correlation
submit_email is highly correlated with port and 38 other fieldsHigh correlation
ratio_intRedirection is highly correlated with port and 38 other fieldsHigh correlation
domain_with_copyright is highly correlated with sfh and 5 other fieldsHigh correlation
punycode is highly correlated with sfh and 5 other fieldsHigh correlation
prefix_suffix is highly correlated with sfh and 5 other fieldsHigh correlation
special_characters is highly correlated with port and 38 other fieldsHigh correlation
onmouseover is highly correlated with sfh and 5 other fieldsHigh correlation
whois_registered_domain is highly correlated with sfh and 5 other fieldsHigh correlation
right_clic is highly correlated with sfh and 5 other fieldsHigh correlation
tld_in_subdomain is highly correlated with ip and 1 other fieldsHigh correlation
ratio_intErrors is highly correlated with port and 38 other fieldsHigh correlation
domain_in_title is highly correlated with statusHigh correlation
https is highly correlated with sfh and 5 other fieldsHigh correlation
popup_window is highly correlated with sfh and 5 other fieldsHigh correlation
nb_subdomains is highly correlated with nb_wwwHigh correlation
path_extension is highly correlated with sfh and 5 other fieldsHigh correlation
nb_external_redirection is highly correlated with abnormal_subdomain and 2 other fieldsHigh correlation
abnormal_subdomain is highly correlated with nb_external_redirection and 1 other fieldsHigh correlation
iframe is highly correlated with sfh and 5 other fieldsHigh correlation
statistical_report is highly correlated with sfh and 5 other fieldsHigh correlation
brand_in_subdomain is highly correlated with sfh and 5 other fieldsHigh correlation
tld_in_path is highly correlated with sfh and 5 other fieldsHigh correlation
login_form is highly correlated with sfh and 5 other fieldsHigh correlation
nb_com is highly correlated with http_in_path and 3 other fieldsHigh correlation
phish_hints is highly correlated with nb_com and 3 other fieldsHigh correlation
web_traffic is highly correlated with tld_in_subdomainHigh correlation
nb_extCSS is highly skewed (γ1 = 23.49547911) Skewed
url is uniformly distributed Uniform
status is uniformly distributed Uniform
nb_com has 10103 (88.4%) zeros Zeros
nb_redirection has 6775 (59.3%) zeros Zeros
char_repeat has 2361 (20.7%) zeros Zeros
shortest_word_path has 3215 (28.1%) zeros Zeros
longest_word_path has 3215 (28.1%) zeros Zeros
avg_word_path has 3215 (28.1%) zeros Zeros
phish_hints has 9389 (82.1%) zeros Zeros
nb_hyperlinks has 1381 (12.1%) zeros Zeros
ratio_intHyperlinks has 1886 (16.5%) zeros Zeros
ratio_extHyperlinks has 3071 (26.9%) zeros Zeros
nb_extCSS has 7828 (68.5%) zeros Zeros
ratio_extRedirection has 6143 (53.7%) zeros Zeros
ratio_extErrors has 8121 (71.0%) zeros Zeros
links_in_tags has 3403 (29.8%) zeros Zeros
ratio_intMedia has 5469 (47.8%) zeros Zeros
ratio_extMedia has 7335 (64.2%) zeros Zeros
safe_anchor has 4438 (38.8%) zeros Zeros
domain_registration_length has 1404 (12.3%) zeros Zeros
web_traffic has 4444 (38.9%) zeros Zeros
page_rank has 2666 (23.3%) zeros Zeros
digits has 6566 (57.4%) zeros Zeros

Reproduction

Analysis started2023-02-03 00:34:54.694867
Analysis finished2023-02-03 00:37:08.561974
Duration2 minutes and 13.87 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

url
Categorical

HIGH CARDINALITY
UNIFORM

Distinct11429
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
http://e710z0ear.du.r.appspot.com/c:/users/user/downlo
 
2
http://www.crestonwood.com/router.php
 
1
https://www.riverbed.com/products/steelcentral/network-performance-management/steelcentral-packet-analyzer.html
 
1
http://fience.vot.pl/xl
 
1
https://usbank.app.link/NquAmzCW01?platform=hootsuite
 
1
Other values (11424)
11424 

Length

Max length1641
Median length439
Mean length61.120035
Min length12

Characters and Unicode

Total characters698602
Distinct characters100
Distinct categories14 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11428 ?
Unique (%)> 99.9%

Sample

1st rowhttp://www.crestonwood.com/router.php
2nd rowhttp://shadetreetechnology.com/V4/validation/a111aedc8ae390eabcfa130e041a10a4
3rd rowhttps://support-appleld.com.secureupdate.duilawyeryork.com/ap/89e6a3b4b063b8d/?cmd=_update&dispatch=89e6a3b4b063b8d1b&locale=_
4th rowhttp://rgipt.ac.in
5th rowhttp://www.iracing.com/tracks/gateway-motorsports-park/

Common Values

ValueCountFrequency (%)
http://e710z0ear.du.r.appspot.com/c:/users/user/downlo2
 
< 0.1%
http://www.crestonwood.com/router.php1
 
< 0.1%
https://www.riverbed.com/products/steelcentral/network-performance-management/steelcentral-packet-analyzer.html1
 
< 0.1%
http://fience.vot.pl/xl1
 
< 0.1%
https://usbank.app.link/NquAmzCW01?platform=hootsuite1
 
< 0.1%
http://103.229.125.10/1
 
< 0.1%
https://en.wikiquote.org/wiki/India1
 
< 0.1%
http://chasebank.com66.henrybakercollege.edu.in/chase.com/update.php1
 
< 0.1%
http://searchwindevelopment.techtarget.com/definition/domain-name1
 
< 0.1%
http://connxupdate.be/myebranch.iccu.com/Idaho%20Central%20Credit%20Union.php1
 
< 0.1%
Other values (11419)11419
99.9%

Length

2023-02-02T18:37:08.732089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
http://stolizaparketa.ru/wp-content/themes/twentyfifteen/css/read/chinavali/index.php?email=_xxx@yyy.com7
 
0.1%
http://153284594738391.statictab.com/25060803
 
< 0.1%
http://e710z0ear.du.r.appspot.com/c:/users/user/downlo2
 
< 0.1%
http://www.paypal-verification.applmanager.com/customer_center/user-4787412
 
< 0.1%
http://tokokainbandung.com/wp-content/themes/theretailer/inc/addons/login/customer_center/customer-idpp00c672/myaccount/signin2
 
< 0.1%
https://sites.google.com/site/recoveryfbconfirmcontactus2
 
< 0.1%
https://milenyumpark.com.tr/iletisim2
 
< 0.1%
http://www.courgeon-immobilier.fr/wp-content/uploads/2019/07/tpg/fa2acd5487b1bef895a7453e5dc960132
 
< 0.1%
https://www.zabor-vn.com/system/csvprice_pro/smart/customer_center/customer-idpp00c354/myaccount/signin2
 
< 0.1%
https://elhagearms.com/jppp/toda2
 
< 0.1%
Other values (11244)11407
99.8%

Most occurring characters

ValueCountFrequency (%)
t49171
 
7.0%
/49030
 
7.0%
e40829
 
5.8%
o35303
 
5.1%
a32792
 
4.7%
p30923
 
4.4%
s29208
 
4.2%
c28485
 
4.1%
.28354
 
4.1%
i27617
 
4.0%
Other values (90)346890
49.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter492123
70.4%
Other Punctuation95110
 
13.6%
Decimal Number62318
 
8.9%
Uppercase Letter30171
 
4.3%
Dash Punctuation11402
 
1.6%
Connector Punctuation3688
 
0.5%
Math Symbol3552
 
0.5%
Control65
 
< 0.1%
Open Punctuation64
 
< 0.1%
Close Punctuation63
 
< 0.1%
Other values (4)46
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1951
 
6.5%
D1674
 
5.5%
S1508
 
5.0%
C1502
 
5.0%
F1499
 
5.0%
E1399
 
4.6%
B1368
 
4.5%
N1287
 
4.3%
T1274
 
4.2%
M1266
 
4.2%
Other values (18)15443
51.2%
Lowercase Letter
ValueCountFrequency (%)
t49171
 
10.0%
e40829
 
8.3%
o35303
 
7.2%
a32792
 
6.7%
p30923
 
6.3%
s29208
 
5.9%
c28485
 
5.8%
i27617
 
5.6%
r23269
 
4.7%
n22460
 
4.6%
Other values (17)172066
35.0%
Other Punctuation
ValueCountFrequency (%)
/49030
51.6%
.28354
29.8%
:11749
 
12.4%
&1855
 
2.0%
?1614
 
1.7%
%1407
 
1.5%
;712
 
0.7%
@254
 
0.3%
#50
 
0.1%
,46
 
< 0.1%
Other values (3)39
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
28092
13.0%
07889
12.7%
17375
11.8%
36154
9.9%
45713
9.2%
75622
9.0%
55578
9.0%
65386
8.6%
85319
8.5%
95190
8.3%
Math Symbol
ValueCountFrequency (%)
=3351
94.3%
+120
 
3.4%
~78
 
2.2%
<3
 
0.1%
Open Punctuation
ValueCountFrequency (%)
(45
70.3%
[10
 
15.6%
{9
 
14.1%
Close Punctuation
ValueCountFrequency (%)
)45
71.4%
]10
 
15.9%
}8
 
12.7%
Control
ValueCountFrequency (%)
‚33
50.8%
ƒ31
47.7%
‘1
 
1.5%
Modifier Symbol
ValueCountFrequency (%)
`17
89.5%
^2
 
10.5%
Space Separator
ValueCountFrequency (%)
 2
66.7%
1
33.3%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Dash Punctuation
ValueCountFrequency (%)
-11402
100.0%
Connector Punctuation
ValueCountFrequency (%)
_3688
100.0%
Currency Symbol
ValueCountFrequency (%)
$22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin522293
74.8%
Common176307
 
25.2%
Han2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t49171
 
9.4%
e40829
 
7.8%
o35303
 
6.8%
a32792
 
6.3%
p30923
 
5.9%
s29208
 
5.6%
c28485
 
5.5%
i27617
 
5.3%
r23269
 
4.5%
n22460
 
4.3%
Other values (44)202236
38.7%
Common
ValueCountFrequency (%)
/49030
27.8%
.28354
16.1%
:11749
 
6.7%
-11402
 
6.5%
28092
 
4.6%
07889
 
4.5%
17375
 
4.2%
36154
 
3.5%
45713
 
3.2%
75622
 
3.2%
Other values (34)34927
19.8%
Han
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII698466
> 99.9%
None134
 
< 0.1%
CJK2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t49171
 
7.0%
/49030
 
7.0%
e40829
 
5.8%
o35303
 
5.1%
a32792
 
4.7%
p30923
 
4.4%
s29208
 
4.2%
c28485
 
4.1%
.28354
 
4.1%
i27617
 
4.0%
Other values (81)346754
49.6%
None
ValueCountFrequency (%)
‚33
24.6%
Â33
24.6%
Ã33
24.6%
ƒ31
23.1%
 2
 
1.5%
µ1
 
0.7%
‘1
 
0.7%
CJK
ValueCountFrequency (%)
1
50.0%
1
50.0%

ip
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
9709 
1
1721 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09709
84.9%
11721
 
15.1%

Length

2023-02-02T18:37:08.860463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:08.985321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
09709
84.9%
11721
 
15.1%

Most occurring characters

ValueCountFrequency (%)
09709
84.9%
11721
 
15.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09709
84.9%
11721
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09709
84.9%
11721
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09709
84.9%
11721
 
15.1%

nb_www
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
6330 
1
5074 
2
 
26

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
06330
55.4%
15074
44.4%
226
 
0.2%

Length

2023-02-02T18:37:09.072552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:09.185543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
06330
55.4%
15074
44.4%
226
 
0.2%

Most occurring characters

ValueCountFrequency (%)
06330
55.4%
15074
44.4%
226
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06330
55.4%
15074
44.4%
226
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06330
55.4%
15074
44.4%
226
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06330
55.4%
15074
44.4%
226
 
0.2%

nb_com
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1279965004
Minimum0
Maximum6
Zeros10103
Zeros (%)88.4%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:09.338075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3790078643
Coefficient of variation (CV)2.961079897
Kurtosis23.44330347
Mean0.1279965004
Median Absolute Deviation (MAD)0
Skewness3.778379347
Sum1463
Variance0.1436469612
MonotonicityNot monotonic
2023-02-02T18:37:09.429055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
010103
88.4%
11220
 
10.7%
289
 
0.8%
312
 
0.1%
43
 
< 0.1%
62
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
010103
88.4%
11220
 
10.7%
289
 
0.8%
312
 
0.1%
43
 
< 0.1%
51
 
< 0.1%
62
 
< 0.1%
ValueCountFrequency (%)
62
 
< 0.1%
51
 
< 0.1%
43
 
< 0.1%
312
 
0.1%
289
 
0.8%
11220
 
10.7%
010103
88.4%

nb_dslash
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11355 
1
 
75

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011355
99.3%
175
 
0.7%

Length

2023-02-02T18:37:09.539019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:09.664325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011355
99.3%
175
 
0.7%

Most occurring characters

ValueCountFrequency (%)
011355
99.3%
175
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011355
99.3%
175
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011355
99.3%
175
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011355
99.3%
175
 
0.7%

http_in_path
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11280 
1
 
129
2
 
10
4
 
9
3
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011280
98.7%
1129
 
1.1%
210
 
0.1%
49
 
0.1%
32
 
< 0.1%

Length

2023-02-02T18:37:09.777386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:09.894507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011280
98.7%
1129
 
1.1%
210
 
0.1%
49
 
0.1%
32
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
011280
98.7%
1129
 
1.1%
210
 
0.1%
49
 
0.1%
32
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011280
98.7%
1129
 
1.1%
210
 
0.1%
49
 
0.1%
32
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011280
98.7%
1129
 
1.1%
210
 
0.1%
49
 
0.1%
32
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011280
98.7%
1129
 
1.1%
210
 
0.1%
49
 
0.1%
32
 
< 0.1%

punycode
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11426 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011426
> 99.9%
14
 
< 0.1%

Length

2023-02-02T18:37:10.001382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:10.089880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011426
> 99.9%
14
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
011426
> 99.9%
14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011426
> 99.9%
14
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011426
> 99.9%
14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011426
> 99.9%
14
 
< 0.1%

port
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11403 
1
 
27

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011403
99.8%
127
 
0.2%

Length

2023-02-02T18:37:10.168910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:10.256404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011403
99.8%
127
 
0.2%

Most occurring characters

ValueCountFrequency (%)
011403
99.8%
127
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011403
99.8%
127
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011403
99.8%
127
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011403
99.8%
127
 
0.2%

tld_in_path
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
10680 
1
 
750

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010680
93.4%
1750
 
6.6%

Length

2023-02-02T18:37:10.334304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:10.425698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
010680
93.4%
1750
 
6.6%

Most occurring characters

ValueCountFrequency (%)
010680
93.4%
1750
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010680
93.4%
1750
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010680
93.4%
1750
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010680
93.4%
1750
 
6.6%

tld_in_subdomain
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
10857 
1
 
573

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010857
95.0%
1573
 
5.0%

Length

2023-02-02T18:37:10.503987image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:10.601935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
010857
95.0%
1573
 
5.0%

Most occurring characters

ValueCountFrequency (%)
010857
95.0%
1573
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010857
95.0%
1573
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010857
95.0%
1573
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010857
95.0%
1573
 
5.0%

abnormal_subdomain
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11183 
1
 
247

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011183
97.8%
1247
 
2.2%

Length

2023-02-02T18:37:10.684267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:10.772821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011183
97.8%
1247
 
2.2%

Most occurring characters

ValueCountFrequency (%)
011183
97.8%
1247
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011183
97.8%
1247
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011183
97.8%
1247
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011183
97.8%
1247
 
2.2%

nb_subdomains
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
2
6178 
3
3950 
1
1302 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row1
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
26178
54.1%
33950
34.6%
11302
 
11.4%

Length

2023-02-02T18:37:10.856444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:10.948840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
26178
54.1%
33950
34.6%
11302
 
11.4%

Most occurring characters

ValueCountFrequency (%)
26178
54.1%
33950
34.6%
11302
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
26178
54.1%
33950
34.6%
11302
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
26178
54.1%
33950
34.6%
11302
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
26178
54.1%
33950
34.6%
11302
 
11.4%

prefix_suffix
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
9116 
1
2314 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
09116
79.8%
12314
 
20.2%

Length

2023-02-02T18:37:11.045228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:11.138675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
09116
79.8%
12314
 
20.2%

Most occurring characters

ValueCountFrequency (%)
09116
79.8%
12314
 
20.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
09116
79.8%
12314
 
20.2%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
09116
79.8%
12314
 
20.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
09116
79.8%
12314
 
20.2%

random_domain
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
10478 
1
 
952

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010478
91.7%
1952
 
8.3%

Length

2023-02-02T18:37:11.219403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:11.304123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
010478
91.7%
1952
 
8.3%

Most occurring characters

ValueCountFrequency (%)
010478
91.7%
1952
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010478
91.7%
1952
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010478
91.7%
1952
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010478
91.7%
1952
 
8.3%

shortening_service
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
10019 
1
1411 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010019
87.7%
11411
 
12.3%

Length

2023-02-02T18:37:11.376281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:11.466361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
010019
87.7%
11411
 
12.3%

Most occurring characters

ValueCountFrequency (%)
010019
87.7%
11411
 
12.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010019
87.7%
11411
 
12.3%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010019
87.7%
11411
 
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010019
87.7%
11411
 
12.3%

path_extension
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11428 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011428
> 99.9%
12
 
< 0.1%

Length

2023-02-02T18:37:11.548357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:11.647787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011428
> 99.9%
12
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
011428
> 99.9%
12
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011428
> 99.9%
12
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011428
> 99.9%
12
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011428
> 99.9%
12
 
< 0.1%

nb_redirection
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4982502187
Minimum0
Maximum6
Zeros6775
Zeros (%)59.3%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:11.723555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6919069557
Coefficient of variation (CV)1.388673662
Kurtosis3.524335378
Mean0.4982502187
Median Absolute Deviation (MAD)0
Skewness1.568452476
Sum5695
Variance0.4787352353
MonotonicityNot monotonic
2023-02-02T18:37:11.800119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
06775
59.3%
13827
33.5%
2662
 
5.8%
3128
 
1.1%
431
 
0.3%
56
 
0.1%
61
 
< 0.1%
ValueCountFrequency (%)
06775
59.3%
13827
33.5%
2662
 
5.8%
3128
 
1.1%
431
 
0.3%
56
 
0.1%
61
 
< 0.1%
ValueCountFrequency (%)
61
 
< 0.1%
56
 
0.1%
431
 
0.3%
3128
 
1.1%
2662
 
5.8%
13827
33.5%
06775
59.3%

nb_external_redirection
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11394 
1
 
36

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011394
99.7%
136
 
0.3%

Length

2023-02-02T18:37:11.896062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:11.993711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011394
99.7%
136
 
0.3%

Most occurring characters

ValueCountFrequency (%)
011394
99.7%
136
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011394
99.7%
136
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011394
99.7%
136
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011394
99.7%
136
 
0.3%

length_words_raw
Real number (ℝ≥0)

HIGH CORRELATION

Distinct54
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.232808399
Minimum1
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:12.087640image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median5
Q38
95-th percentile14
Maximum106
Range105
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.572355118
Coefficient of variation (CV)0.8940360045
Kurtosis60.7624273
Mean6.232808399
Median Absolute Deviation (MAD)3
Skewness5.36734996
Sum71241
Variance31.05114156
MonotonicityNot monotonic
2023-02-02T18:37:12.226551image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22556
22.4%
41157
10.1%
51094
9.6%
61088
9.5%
31080
9.4%
7823
 
7.2%
8647
 
5.7%
9498
 
4.4%
10419
 
3.7%
13370
 
3.2%
Other values (44)1698
14.9%
ValueCountFrequency (%)
1354
 
3.1%
22556
22.4%
31080
9.4%
41157
10.1%
51094
9.6%
61088
9.5%
7823
 
7.2%
8647
 
5.7%
9498
 
4.4%
10419
 
3.7%
ValueCountFrequency (%)
1061
 
< 0.1%
962
 
< 0.1%
902
 
< 0.1%
811
 
< 0.1%
806
0.1%
681
 
< 0.1%
644
< 0.1%
631
 
< 0.1%
612
 
< 0.1%
601
 
< 0.1%

char_repeat
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct55
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.927471566
Minimum0
Maximum146
Zeros2361
Zeros (%)20.7%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:12.368367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile7
Maximum146
Range146
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.768935795
Coefficient of variation (CV)1.629028903
Kurtosis393.756615
Mean2.927471566
Median Absolute Deviation (MAD)2
Skewness15.75678114
Sum33461
Variance22.74274862
MonotonicityNot monotonic
2023-02-02T18:37:12.472513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33148
27.5%
02361
20.7%
11712
15.0%
41677
14.7%
2875
 
7.7%
5679
 
5.9%
6381
 
3.3%
7164
 
1.4%
8123
 
1.1%
960
 
0.5%
Other values (45)250
 
2.2%
ValueCountFrequency (%)
02361
20.7%
11712
15.0%
2875
 
7.7%
33148
27.5%
41677
14.7%
5679
 
5.9%
6381
 
3.3%
7164
 
1.4%
8123
 
1.1%
960
 
0.5%
ValueCountFrequency (%)
1462
< 0.1%
1451
< 0.1%
1441
< 0.1%
1411
< 0.1%
1011
< 0.1%
961
< 0.1%
851
< 0.1%
661
< 0.1%
611
< 0.1%
592
< 0.1%

shortest_words_raw
Real number (ℝ≥0)

HIGH CORRELATION

Distinct25
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.127296588
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:12.585629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile8
Maximum31
Range30
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.211571305
Coefficient of variation (CV)0.7071831031
Kurtosis15.7258904
Mean3.127296588
Median Absolute Deviation (MAD)1
Skewness3.156658185
Sum35745
Variance4.891047639
MonotonicityNot monotonic
2023-02-02T18:37:12.689445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
34730
41.4%
23112
27.2%
11522
 
13.3%
4631
 
5.5%
8411
 
3.6%
5265
 
2.3%
6264
 
2.3%
7160
 
1.4%
9102
 
0.9%
1157
 
0.5%
Other values (15)176
 
1.5%
ValueCountFrequency (%)
11522
 
13.3%
23112
27.2%
34730
41.4%
4631
 
5.5%
5265
 
2.3%
6264
 
2.3%
7160
 
1.4%
8411
 
3.6%
9102
 
0.9%
1044
 
0.4%
ValueCountFrequency (%)
311
 
< 0.1%
271
 
< 0.1%
241
 
< 0.1%
231
 
< 0.1%
214
 
< 0.1%
206
0.1%
196
0.1%
186
0.1%
177
0.1%
1613
0.1%

shortest_word_host
Real number (ℝ≥0)

HIGH CORRELATION

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.019772528
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:12.811280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q36
95-th percentile13
Maximum39
Range38
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.94157981
Coefficient of variation (CV)0.7852108412
Kurtosis6.957438136
Mean5.019772528
Median Absolute Deviation (MAD)0
Skewness2.267936107
Sum57376
Variance15.5360514
MonotonicityNot monotonic
2023-02-02T18:37:12.923371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
35745
50.3%
2873
 
7.6%
8634
 
5.5%
4601
 
5.3%
5556
 
4.9%
6474
 
4.1%
7439
 
3.8%
1408
 
3.6%
9322
 
2.8%
10285
 
2.5%
Other values (24)1093
 
9.6%
ValueCountFrequency (%)
1408
 
3.6%
2873
 
7.6%
35745
50.3%
4601
 
5.3%
5556
 
4.9%
6474
 
4.1%
7439
 
3.8%
8634
 
5.5%
9322
 
2.8%
10285
 
2.5%
ValueCountFrequency (%)
394
< 0.1%
381
 
< 0.1%
331
 
< 0.1%
312
< 0.1%
302
< 0.1%
293
< 0.1%
282
< 0.1%
271
 
< 0.1%
263
< 0.1%
254
< 0.1%

shortest_word_path
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.398950131
Minimum0
Maximum40
Zeros3215
Zeros (%)28.1%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:13.045233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile7
Maximum40
Range40
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.997808681
Coefficient of variation (CV)1.249633597
Kurtosis38.27818007
Mean2.398950131
Median Absolute Deviation (MAD)1
Skewness4.687603636
Sum27420
Variance8.986856891
MonotonicityNot monotonic
2023-02-02T18:37:13.152417image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
03215
28.1%
22617
22.9%
31941
17.0%
41258
 
11.0%
11162
 
10.2%
5354
 
3.1%
6297
 
2.6%
7158
 
1.4%
8105
 
0.9%
1074
 
0.6%
Other values (23)249
 
2.2%
ValueCountFrequency (%)
03215
28.1%
11162
 
10.2%
22617
22.9%
31941
17.0%
41258
 
11.0%
5354
 
3.1%
6297
 
2.6%
7158
 
1.4%
8105
 
0.9%
966
 
0.6%
ValueCountFrequency (%)
402
 
< 0.1%
372
 
< 0.1%
361
 
< 0.1%
3228
0.2%
301
 
< 0.1%
292
 
< 0.1%
283
 
< 0.1%
261
 
< 0.1%
241
 
< 0.1%
233
 
< 0.1%

longest_words_raw
Real number (ℝ≥0)

HIGH CORRELATION

Distinct119
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.39387577
Minimum2
Maximum829
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:13.259037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q19
median11
Q316
95-th percentile32
Maximum829
Range827
Interquartile range (IQR)7

Descriptive statistics

Standard deviation22.0836445
Coefficient of variation (CV)1.434573387
Kurtosis294.7805804
Mean15.39387577
Median Absolute Deviation (MAD)3
Skewness13.53102354
Sum175952
Variance487.6873542
MonotonicityNot monotonic
2023-02-02T18:37:13.376717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91163
 
10.2%
101143
 
10.0%
11983
 
8.6%
8951
 
8.3%
12823
 
7.2%
13726
 
6.4%
7700
 
6.1%
17634
 
5.5%
14554
 
4.8%
15514
 
4.5%
Other values (109)3239
28.3%
ValueCountFrequency (%)
25
 
< 0.1%
3106
 
0.9%
4135
 
1.2%
5286
 
2.5%
6416
 
3.6%
7700
6.1%
8951
8.3%
91163
10.2%
101143
10.0%
11983
8.6%
ValueCountFrequency (%)
8291
 
< 0.1%
5071
 
< 0.1%
4921
 
< 0.1%
4871
 
< 0.1%
4061
 
< 0.1%
3833
 
< 0.1%
3013
 
< 0.1%
3003
 
< 0.1%
28810
0.1%
2401
 
< 0.1%

longest_word_host
Real number (ℝ≥0)

HIGH CORRELATION

Distinct49
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.467979
Minimum1
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:13.489511image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median10
Q313
95-th percentile19
Maximum62
Range61
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.932014659
Coefficient of variation (CV)0.4711525174
Kurtosis7.201449994
Mean10.467979
Median Absolute Deviation (MAD)3
Skewness1.631076325
Sum119649
Variance24.3247686
MonotonicityNot monotonic
2023-02-02T18:37:13.603194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
91176
10.3%
81061
 
9.3%
71034
 
9.0%
10994
 
8.7%
13970
 
8.5%
6910
 
8.0%
11836
 
7.3%
12691
 
6.0%
5581
 
5.1%
14520
 
4.5%
Other values (39)2657
23.2%
ValueCountFrequency (%)
116
 
0.1%
237
 
0.3%
3344
 
3.0%
4378
 
3.3%
5581
5.1%
6910
8.0%
71034
9.0%
81061
9.3%
91176
10.3%
10994
8.7%
ValueCountFrequency (%)
621
 
< 0.1%
611
 
< 0.1%
601
 
< 0.1%
541
 
< 0.1%
471
 
< 0.1%
442
< 0.1%
432
< 0.1%
423
< 0.1%
411
 
< 0.1%
401
 
< 0.1%

longest_word_path
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct120
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.56150481
Minimum0
Maximum829
Zeros3215
Zeros (%)28.1%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:13.727656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q311
95-th percentile32
Maximum829
Range829
Interquartile range (IQR)11

Descriptive statistics

Standard deviation23.07788337
Coefficient of variation (CV)2.185094244
Kurtosis256.859316
Mean10.56150481
Median Absolute Deviation (MAD)5
Skewness12.39572976
Sum120718
Variance532.5887009
MonotonicityNot monotonic
2023-02-02T18:37:13.845653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03215
28.1%
7983
 
8.6%
8930
 
8.1%
10794
 
6.9%
9747
 
6.5%
6539
 
4.7%
5538
 
4.7%
11468
 
4.1%
17405
 
3.5%
32380
 
3.3%
Other values (110)2431
21.3%
ValueCountFrequency (%)
03215
28.1%
141
 
0.4%
2130
 
1.1%
3157
 
1.4%
4306
 
2.7%
5538
 
4.7%
6539
 
4.7%
7983
 
8.6%
8930
 
8.1%
9747
 
6.5%
ValueCountFrequency (%)
8291
 
< 0.1%
5071
 
< 0.1%
4921
 
< 0.1%
4871
 
< 0.1%
4061
 
< 0.1%
3833
 
< 0.1%
3013
 
< 0.1%
3003
 
< 0.1%
28810
0.1%
2401
 
< 0.1%

avg_words_raw
Real number (ℝ≥0)

HIGH CORRELATION

Distinct896
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.258881692
Minimum2
Maximum128.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:13.989037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15.25
median6.5
Q38
95-th percentile13.2
Maximum128.25
Range126.25
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation4.145826506
Coefficient of variation (CV)0.5711384593
Kurtosis189.4561432
Mean7.258881692
Median Absolute Deviation (MAD)1.5
Skewness9.548721745
Sum82969.01774
Variance17.18787742
MonotonicityNot monotonic
2023-02-02T18:37:14.103599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6536
 
4.7%
5494
 
4.3%
7465
 
4.1%
8369
 
3.2%
6.5342
 
3.0%
5.5317
 
2.8%
9255
 
2.2%
4245
 
2.1%
4.5238
 
2.1%
7.5234
 
2.0%
Other values (886)7935
69.4%
ValueCountFrequency (%)
214
0.1%
2.1428571431
 
< 0.1%
2.2352941181
 
< 0.1%
2.253
 
< 0.1%
2.2631578951
 
< 0.1%
2.2926829271
 
< 0.1%
2.32
 
< 0.1%
2.3333333332
 
< 0.1%
2.3751
 
< 0.1%
2.4285714293
 
< 0.1%
ValueCountFrequency (%)
128.251
 
< 0.1%
106.51
 
< 0.1%
1003
< 0.1%
83.363636361
 
< 0.1%
741
 
< 0.1%
68.1251
 
< 0.1%
612
< 0.1%
47.251
 
< 0.1%
45.752
< 0.1%
42.6251
 
< 0.1%

avg_word_host
Real number (ℝ≥0)

HIGH CORRELATION

Distinct174
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.678074522
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:14.222377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.5
Q15.25
median7
Q39
95-th percentile14.5
Maximum39
Range38
Interquartile range (IQR)3.75

Descriptive statistics

Standard deviation3.578434671
Coefficient of variation (CV)0.4660588616
Kurtosis5.93341247
Mean7.678074522
Median Absolute Deviation (MAD)2
Skewness1.746079562
Sum87760.39179
Variance12.80519469
MonotonicityNot monotonic
2023-02-02T18:37:14.337406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5845
 
7.4%
5.5828
 
7.2%
6796
 
7.0%
7765
 
6.7%
8665
 
5.8%
6.5573
 
5.0%
4504
 
4.4%
9481
 
4.2%
7.5474
 
4.1%
4.5461
 
4.0%
Other values (164)5038
44.1%
ValueCountFrequency (%)
116
 
0.1%
1.51
 
< 0.1%
1.6666666677
 
0.1%
1.752
 
< 0.1%
239
0.3%
2.2518
 
0.2%
2.3333333336
 
0.1%
2.561
0.5%
2.61
 
< 0.1%
2.66666666710
 
0.1%
ValueCountFrequency (%)
394
< 0.1%
381
 
< 0.1%
331
 
< 0.1%
312
< 0.1%
302
< 0.1%
29.51
 
< 0.1%
293
< 0.1%
282
< 0.1%
271
 
< 0.1%
263
< 0.1%

avg_word_path
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct757
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.092424702
Minimum0
Maximum250
Zeros3215
Zeros (%)28.1%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:14.471605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.857142857
Q36.714285714
95-th percentile13
Maximum250
Range250
Interquartile range (IQR)6.714285714

Descriptive statistics

Standard deviation7.14704972
Coefficient of variation (CV)1.403466941
Kurtosis336.1082963
Mean5.092424702
Median Absolute Deviation (MAD)2.476190476
Skewness13.44674137
Sum58206.41434
Variance51.0803197
MonotonicityNot monotonic
2023-02-02T18:37:14.585395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03215
28.1%
5470
 
4.1%
6406
 
3.6%
4397
 
3.5%
7266
 
2.3%
7.375217
 
1.9%
4.5215
 
1.9%
5.5209
 
1.8%
3189
 
1.7%
6.5159
 
1.4%
Other values (747)5687
49.8%
ValueCountFrequency (%)
03215
28.1%
141
 
0.4%
1.53
 
< 0.1%
1.6666666672
 
< 0.1%
2140
 
1.2%
2.0512820511
 
< 0.1%
2.1111111111
 
< 0.1%
2.1666666671
 
< 0.1%
2.2363636361
 
< 0.1%
2.254
 
< 0.1%
ValueCountFrequency (%)
2501
 
< 0.1%
2061
 
< 0.1%
194.53
< 0.1%
118.52
< 0.1%
115.41
 
< 0.1%
103.51
 
< 0.1%
96.222222221
 
< 0.1%
86.833333331
 
< 0.1%
86.53
< 0.1%
72.53
< 0.1%

phish_hints
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3277340332
Minimum0
Maximum10
Zeros9389
Zeros (%)82.1%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:14.701033image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.842600352
Coefficient of variation (CV)2.57098826
Kurtosis12.5187366
Mean0.3277340332
Median Absolute Deviation (MAD)0
Skewness3.216484397
Sum3746
Variance0.7099753532
MonotonicityNot monotonic
2023-02-02T18:37:14.794414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
09389
82.1%
11079
 
9.4%
2460
 
4.0%
3325
 
2.8%
4136
 
1.2%
528
 
0.2%
69
 
0.1%
102
 
< 0.1%
72
 
< 0.1%
ValueCountFrequency (%)
09389
82.1%
11079
 
9.4%
2460
 
4.0%
3325
 
2.8%
4136
 
1.2%
528
 
0.2%
69
 
0.1%
72
 
< 0.1%
102
 
< 0.1%
ValueCountFrequency (%)
102
 
< 0.1%
72
 
< 0.1%
69
 
0.1%
528
 
0.2%
4136
 
1.2%
3325
 
2.8%
2460
 
4.0%
11079
 
9.4%
09389
82.1%

domain_in_brand
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
10239 
1
1191 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010239
89.6%
11191
 
10.4%

Length

2023-02-02T18:37:14.947484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:15.088598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
010239
89.6%
11191
 
10.4%

Most occurring characters

ValueCountFrequency (%)
010239
89.6%
11191
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010239
89.6%
11191
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010239
89.6%
11191
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010239
89.6%
11191
 
10.4%

brand_in_subdomain
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11383 
1
 
47

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011383
99.6%
147
 
0.4%

Length

2023-02-02T18:37:15.172106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:15.331448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011383
99.6%
147
 
0.4%

Most occurring characters

ValueCountFrequency (%)
011383
99.6%
147
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011383
99.6%
147
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011383
99.6%
147
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011383
99.6%
147
 
0.4%

brand_in_path
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11374 
1
 
56

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011374
99.5%
156
 
0.5%

Length

2023-02-02T18:37:15.460596image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:15.592507image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011374
99.5%
156
 
0.5%

Most occurring characters

ValueCountFrequency (%)
011374
99.5%
156
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011374
99.5%
156
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011374
99.5%
156
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011374
99.5%
156
 
0.5%

suspecious_tld
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11225 
1
 
205

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011225
98.2%
1205
 
1.8%

Length

2023-02-02T18:37:15.684577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:15.783016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011225
98.2%
1205
 
1.8%

Most occurring characters

ValueCountFrequency (%)
011225
98.2%
1205
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011225
98.2%
1205
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011225
98.2%
1205
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011225
98.2%
1205
 
1.8%

statistical_report
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11053 
2
 
306
1
 
71

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011053
96.7%
2306
 
2.7%
171
 
0.6%

Length

2023-02-02T18:37:15.867569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:15.965340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011053
96.7%
2306
 
2.7%
171
 
0.6%

Most occurring characters

ValueCountFrequency (%)
011053
96.7%
2306
 
2.7%
171
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011053
96.7%
2306
 
2.7%
171
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011053
96.7%
2306
 
2.7%
171
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011053
96.7%
2306
 
2.7%
171
 
0.6%

nb_hyperlinks
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct691
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.18976378
Minimum0
Maximum4659
Zeros1381
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:16.053799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median34
Q3101
95-th percentile323
Maximum4659
Range4659
Interquartile range (IQR)92

Descriptive statistics

Standard deviation166.7582535
Coefficient of variation (CV)1.912589807
Kurtosis117.9666629
Mean87.18976378
Median Absolute Deviation (MAD)32
Skewness7.675060655
Sum996579
Variance27808.31511
MonotonicityNot monotonic
2023-02-02T18:37:16.227847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01381
 
12.1%
4344
 
3.0%
51332
 
2.9%
1328
 
2.9%
14258
 
2.3%
21206
 
1.8%
22189
 
1.7%
7171
 
1.5%
8166
 
1.5%
6143
 
1.3%
Other values (681)7912
69.2%
ValueCountFrequency (%)
01381
12.1%
1328
 
2.9%
263
 
0.6%
3113
 
1.0%
4344
 
3.0%
5141
 
1.2%
6143
 
1.3%
7171
 
1.5%
8166
 
1.5%
9122
 
1.1%
ValueCountFrequency (%)
46591
< 0.1%
38221
< 0.1%
31481
< 0.1%
29351
< 0.1%
27261
< 0.1%
22281
< 0.1%
22051
< 0.1%
21691
< 0.1%
19051
< 0.1%
19031
< 0.1%

ratio_intHyperlinks
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct3131
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6024572495
Minimum0
Maximum1
Zeros1886
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:16.354014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2249909388
median0.7434423815
Q30.9447668415
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.7197759027

Descriptive statistics

Standard deviation0.3764744871
Coefficient of variation (CV)0.6248982603
Kurtosis-1.297591151
Mean0.6024572495
Median Absolute Deviation (MAD)0.2523019915
Skewness-0.5280091394
Sum6886.086362
Variance0.1417330395
MonotonicityNot monotonic
2023-02-02T18:37:16.510468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01886
 
16.5%
11690
 
14.8%
0.071428571184
 
1.6%
0.5126
 
1.1%
0.19047619113
 
1.0%
0.66666666798
 
0.9%
0.85714285798
 
0.9%
0.33333333397
 
0.8%
0.7580
 
0.7%
0.18181818266
 
0.6%
Other values (3121)6992
61.2%
ValueCountFrequency (%)
01886
16.5%
0.0088495581
 
< 0.1%
0.0098039221
 
< 0.1%
0.0119284295
 
< 0.1%
0.0156251
 
< 0.1%
0.0164410061
 
< 0.1%
0.0166666671
 
< 0.1%
0.0172413791
 
< 0.1%
0.0210084031
 
< 0.1%
0.0212765961
 
< 0.1%
ValueCountFrequency (%)
11690
14.8%
0.9993593851
 
< 0.1%
0.9984615381
 
< 0.1%
0.9977957381
 
< 0.1%
0.9976726141
 
< 0.1%
0.9972652691
 
< 0.1%
0.996908811
 
< 0.1%
0.996870111
 
< 0.1%
0.9968652041
 
< 0.1%
0.9968025581
 
< 0.1%

ratio_extHyperlinks
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct3131
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2767203533
Minimum0
Maximum1
Zeros3071
Zeros (%)26.9%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:16.689982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.131147541
Q30.4748397435
95-th percentile0.961538462
Maximum1
Range1
Interquartile range (IQR)0.4748397435

Descriptive statistics

Standard deviation0.3199582523
Coefficient of variation (CV)1.156251243
Kurtosis-0.3202850408
Mean0.2767203533
Median Absolute Deviation (MAD)0.131147541
Skewness1.008455407
Sum3162.913638
Variance0.1023732832
MonotonicityNot monotonic
2023-02-02T18:37:16.810697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03071
26.9%
1505
 
4.4%
0.928571429184
 
1.6%
0.5126
 
1.1%
0.80952381113
 
1.0%
0.33333333398
 
0.9%
0.14285714398
 
0.9%
0.66666666797
 
0.8%
0.2580
 
0.7%
0.81818181866
 
0.6%
Other values (3121)6992
61.2%
ValueCountFrequency (%)
03071
26.9%
0.0006406151
 
< 0.1%
0.0015384621
 
< 0.1%
0.0022042621
 
< 0.1%
0.0023273861
 
< 0.1%
0.0027347311
 
< 0.1%
0.003091191
 
< 0.1%
0.003129891
 
< 0.1%
0.0031347961
 
< 0.1%
0.0031974421
 
< 0.1%
ValueCountFrequency (%)
1505
4.4%
0.9911504421
 
< 0.1%
0.9901960781
 
< 0.1%
0.9880715715
 
< 0.1%
0.9843751
 
< 0.1%
0.9835589941
 
< 0.1%
0.9833333331
 
< 0.1%
0.9827586211
 
< 0.1%
0.9789915971
 
< 0.1%
0.9787234041
 
< 0.1%

ratio_nullHyperlinks
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011430
100.0%

Length

2023-02-02T18:37:16.929690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:17.016449image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011430
100.0%

Most occurring characters

ValueCountFrequency (%)
011430
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011430
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011430
100.0%

nb_extCSS
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED
ZEROS

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.784864392
Minimum0
Maximum124
Zeros7828
Zeros (%)68.5%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:17.086425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum124
Range124
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.758801909
Coefficient of variation (CV)3.51500455
Kurtosis887.2521011
Mean0.784864392
Median Absolute Deviation (MAD)0
Skewness23.49547911
Sum8971
Variance7.610987973
MonotonicityNot monotonic
2023-02-02T18:37:17.255548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
07828
68.5%
11860
 
16.3%
2723
 
6.3%
3466
 
4.1%
4164
 
1.4%
5119
 
1.0%
676
 
0.7%
735
 
0.3%
829
 
0.3%
927
 
0.2%
Other values (23)103
 
0.9%
ValueCountFrequency (%)
07828
68.5%
11860
 
16.3%
2723
 
6.3%
3466
 
4.1%
4164
 
1.4%
5119
 
1.0%
676
 
0.7%
735
 
0.3%
829
 
0.3%
927
 
0.2%
ValueCountFrequency (%)
1241
< 0.1%
1231
< 0.1%
951
< 0.1%
712
< 0.1%
381
< 0.1%
312
< 0.1%
271
< 0.1%
251
< 0.1%
241
< 0.1%
231
< 0.1%

ratio_intRedirection
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011430
100.0%

Length

2023-02-02T18:37:17.373744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:17.480786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011430
100.0%

Most occurring characters

ValueCountFrequency (%)
011430
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011430
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011430
100.0%

ratio_extRedirection
Real number (ℝ≥0)

ZEROS

Distinct894
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1589256155
Minimum0
Maximum2
Zeros6143
Zeros (%)53.7%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:17.576547image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.230769231
95-th percentile0.7809610984
Maximum2
Range2
Interquartile range (IQR)0.230769231

Descriptive statistics

Standard deviation0.2664370492
Coefficient of variation (CV)1.676489019
Kurtosis6.639212801
Mean0.1589256155
Median Absolute Deviation (MAD)0
Skewness2.296810119
Sum1816.519785
Variance0.07098870119
MonotonicityNot monotonic
2023-02-02T18:37:19.417743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06143
53.7%
1290
 
2.5%
0.5207
 
1.8%
0.333333333164
 
1.4%
0.25159
 
1.4%
0.285714286145
 
1.3%
0.846153846131
 
1.1%
0.2121
 
1.1%
0.166666667118
 
1.0%
0.125106
 
0.9%
Other values (884)3846
33.6%
ValueCountFrequency (%)
06143
53.7%
0.0024752481
 
< 0.1%
0.0042372881
 
< 0.1%
0.0045248871
 
< 0.1%
0.0063694271
 
< 0.1%
0.0065359481
 
< 0.1%
0.0066666671
 
< 0.1%
0.0068027211
 
< 0.1%
0.0068493153
 
< 0.1%
0.0069444441
 
< 0.1%
ValueCountFrequency (%)
221
0.2%
1.9221967961
 
< 0.1%
1.8108108111
 
< 0.1%
1.58
 
0.1%
1.4545454551
 
< 0.1%
1.4444444441
 
< 0.1%
1.4193548391
 
< 0.1%
1.4117647061
 
< 0.1%
1.3333333332
 
< 0.1%
1.2948717951
 
< 0.1%

ratio_intErrors
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011430
100.0%

Length

2023-02-02T18:37:19.526995image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:19.625697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011430
100.0%

Most occurring characters

ValueCountFrequency (%)
011430
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011430
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011430
100.0%

ratio_extErrors
Real number (ℝ≥0)

ZEROS

Distinct635
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06246862847
Minimum0
Maximum1
Zeros8121
Zeros (%)71.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:19.761289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.034482759
95-th percentile0.4
Maximum1
Range1
Interquartile range (IQR)0.034482759

Descriptive statistics

Standard deviation0.1562086786
Coefficient of variation (CV)2.500594016
Kurtosis13.62793409
Mean0.06246862847
Median Absolute Deviation (MAD)0
Skewness3.510797527
Sum714.0164234
Variance0.02440115125
MonotonicityNot monotonic
2023-02-02T18:37:19.931243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08121
71.0%
0.615384615130
 
1.1%
0.25103
 
0.9%
0.142857143101
 
0.9%
0.297
 
0.8%
0.16666666790
 
0.8%
0.12585
 
0.7%
185
 
0.7%
0.08333333376
 
0.7%
0.33333333375
 
0.7%
Other values (625)2467
 
21.6%
ValueCountFrequency (%)
08121
71.0%
0.002288331
 
< 0.1%
0.0042372881
 
< 0.1%
0.0042918451
 
< 0.1%
0.0043668121
 
< 0.1%
0.0046728971
 
< 0.1%
0.0047169811
 
< 0.1%
0.0049261081
 
< 0.1%
0.0049504951
 
< 0.1%
0.0054347831
 
< 0.1%
ValueCountFrequency (%)
185
0.7%
0.9791666672
 
< 0.1%
0.951
 
< 0.1%
0.9333333331
 
< 0.1%
0.91
 
< 0.1%
0.8754
 
< 0.1%
0.8571428571
 
< 0.1%
0.8518518521
 
< 0.1%
0.8461538461
 
< 0.1%
0.842
 
< 0.1%

login_form
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
10703 
1
 
727

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
010703
93.6%
1727
 
6.4%

Length

2023-02-02T18:37:20.087358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:20.233962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
010703
93.6%
1727
 
6.4%

Most occurring characters

ValueCountFrequency (%)
010703
93.6%
1727
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010703
93.6%
1727
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010703
93.6%
1727
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010703
93.6%
1727
 
6.4%

external_favicon
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
6376 
1
5054 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06376
55.8%
15054
44.2%

Length

2023-02-02T18:37:20.353336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:20.483800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
06376
55.8%
15054
44.2%

Most occurring characters

ValueCountFrequency (%)
06376
55.8%
15054
44.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06376
55.8%
15054
44.2%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06376
55.8%
15054
44.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06376
55.8%
15054
44.2%

links_in_tags
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct473
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.97821079
Minimum0
Maximum100
Zeros3403
Zeros (%)29.8%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:20.628924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median60
Q398.06100357
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)98.06100357

Descriptive statistics

Standard deviation41.52314377
Coefficient of variation (CV)0.7988567351
Kurtosis-1.667946113
Mean51.97821079
Median Absolute Deviation (MAD)40
Skewness-0.1507697409
Sum594110.9493
Variance1724.171468
MonotonicityNot monotonic
2023-02-02T18:37:20.817770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03403
29.8%
1002851
24.9%
50453
 
4.0%
66.66666667345
 
3.0%
75195
 
1.7%
33.33333333169
 
1.5%
80138
 
1.2%
60129
 
1.1%
83.33333333114
 
1.0%
81.81818182106
 
0.9%
Other values (463)3527
30.9%
ValueCountFrequency (%)
03403
29.8%
0.2087682671
 
< 0.1%
0.2092050211
 
< 0.1%
1.3698630141
 
< 0.1%
1.4084507041
 
< 0.1%
1.56251
 
< 0.1%
1.7543859651
 
< 0.1%
1.8867924531
 
< 0.1%
1.9417475731
 
< 0.1%
1.9607843146
 
0.1%
ValueCountFrequency (%)
1002851
24.9%
99.047619051
 
< 0.1%
98.571428571
 
< 0.1%
98.275862071
 
< 0.1%
98.181818182
 
< 0.1%
98.148148151
 
< 0.1%
98.076923081
 
< 0.1%
98.013245031
 
< 0.1%
97.916666671
 
< 0.1%
97.872340431
 
< 0.1%

submit_email
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011430
100.0%

Length

2023-02-02T18:37:20.976991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:21.141521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011430
100.0%

Most occurring characters

ValueCountFrequency (%)
011430
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011430
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011430
100.0%

ratio_intMedia
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct490
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.87044363
Minimum0
Maximum100
Zeros5469
Zeros (%)47.8%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:21.275327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median11.11111111
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)100

Descriptive statistics

Standard deviation46.24989655
Coefficient of variation (CV)1.078829436
Kurtosis-1.82129959
Mean42.87044363
Median Absolute Deviation (MAD)11.11111111
Skewness0.2758129688
Sum490009.1707
Variance2139.05293
MonotonicityNot monotonic
2023-02-02T18:37:21.443460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05469
47.8%
1003461
30.3%
50268
 
2.3%
20149
 
1.3%
80131
 
1.1%
33.33333333110
 
1.0%
66.66666667108
 
0.9%
16.6666666785
 
0.7%
7575
 
0.7%
83.3333333370
 
0.6%
Other values (480)1504
 
13.2%
ValueCountFrequency (%)
05469
47.8%
0.5813953491
 
< 0.1%
0.6557377051
 
< 0.1%
0.8771929821
 
< 0.1%
0.9090909091
 
< 0.1%
0.9345794391
 
< 0.1%
0.9803921573
 
< 0.1%
11
 
< 0.1%
1.1363636361
 
< 0.1%
1.2987012993
 
< 0.1%
ValueCountFrequency (%)
1003461
30.3%
99.785867241
 
< 0.1%
99.69788521
 
< 0.1%
99.658703071
 
< 0.1%
99.651567941
 
< 0.1%
99.602385691
 
< 0.1%
99.494949492
 
< 0.1%
99.492385791
 
< 0.1%
99.479166671
 
< 0.1%
99.457994581
 
< 0.1%

ratio_extMedia
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct490
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.23629302
Minimum0
Maximum100
Zeros7335
Zeros (%)64.2%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:21.610760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q333.33333333
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)33.33333333

Descriptive statistics

Standard deviation38.38657665
Coefficient of variation (CV)1.652009493
Kurtosis-0.1957378857
Mean23.23629302
Median Absolute Deviation (MAD)0
Skewness1.265615285
Sum265590.8293
Variance1473.529267
MonotonicityNot monotonic
2023-02-02T18:37:21.761854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07335
64.2%
1001595
 
14.0%
50268
 
2.3%
80149
 
1.3%
20131
 
1.1%
66.66666667110
 
1.0%
33.33333333108
 
0.9%
83.3333333385
 
0.7%
2575
 
0.7%
16.6666666770
 
0.6%
Other values (480)1504
 
13.2%
ValueCountFrequency (%)
07335
64.2%
0.2141327621
 
< 0.1%
0.3021148041
 
< 0.1%
0.3412969281
 
< 0.1%
0.3484320561
 
< 0.1%
0.3976143141
 
< 0.1%
0.5050505052
 
< 0.1%
0.5076142131
 
< 0.1%
0.5208333331
 
< 0.1%
0.542005421
 
< 0.1%
ValueCountFrequency (%)
1001595
14.0%
99.418604651
 
< 0.1%
99.34426231
 
< 0.1%
99.122807021
 
< 0.1%
99.090909091
 
< 0.1%
99.065420561
 
< 0.1%
99.019607843
 
< 0.1%
991
 
< 0.1%
98.863636361
 
< 0.1%
98.70129873
 
< 0.1%

sfh
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011430
100.0%

Length

2023-02-02T18:37:21.880360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:21.981117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011430
100.0%

Most occurring characters

ValueCountFrequency (%)
011430
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011430
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011430
100.0%

iframe
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11415 
1
 
15

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011415
99.9%
115
 
0.1%

Length

2023-02-02T18:37:22.058496image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:22.156615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011415
99.9%
115
 
0.1%

Most occurring characters

ValueCountFrequency (%)
011415
99.9%
115
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011415
99.9%
115
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011415
99.9%
115
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011415
99.9%
115
 
0.1%

popup_window
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11361 
1
 
69

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011361
99.4%
169
 
0.6%

Length

2023-02-02T18:37:22.249747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:22.348500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011361
99.4%
169
 
0.6%

Most occurring characters

ValueCountFrequency (%)
011361
99.4%
169
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011361
99.4%
169
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011361
99.4%
169
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011361
99.4%
169
 
0.6%

safe_anchor
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct1083
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.06392173
Minimum0
Maximum100
Zeros4438
Zeros (%)38.8%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:22.453580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23.29457364
Q375
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)75

Descriptive statistics

Standard deviation39.07338516
Coefficient of variation (CV)1.054216158
Kurtosis-1.349056923
Mean37.06392173
Median Absolute Deviation (MAD)23.29457364
Skewness0.5130752182
Sum423640.6254
Variance1526.729428
MonotonicityNot monotonic
2023-02-02T18:37:22.597784image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04438
38.8%
1001732
 
15.2%
50337
 
2.9%
25319
 
2.8%
14.28571429225
 
2.0%
33.33333333198
 
1.7%
66.66666667156
 
1.4%
20128
 
1.1%
60105
 
0.9%
75102
 
0.9%
Other values (1073)3690
32.3%
ValueCountFrequency (%)
04438
38.8%
0.179211471
 
< 0.1%
0.645161291
 
< 0.1%
0.7299270071
 
< 0.1%
0.7462686571
 
< 0.1%
0.7751937981
 
< 0.1%
0.9090909091
 
< 0.1%
1.1363636361
 
< 0.1%
1.2345679011
 
< 0.1%
1.251
 
< 0.1%
ValueCountFrequency (%)
1001732
15.2%
99.818840581
 
< 0.1%
99.751243781
 
< 0.1%
99.722222221
 
< 0.1%
99.650145771
 
< 0.1%
99.622166251
 
< 0.1%
99.549549551
 
< 0.1%
99.530127141
 
< 0.1%
99.523809521
 
< 0.1%
99.310344831
 
< 0.1%

onmouseover
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11417 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011417
99.9%
113
 
0.1%

Length

2023-02-02T18:37:22.724206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:22.832740image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011417
99.9%
113
 
0.1%

Most occurring characters

ValueCountFrequency (%)
011417
99.9%
113
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011417
99.9%
113
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011417
99.9%
113
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011417
99.9%
113
 
0.1%

right_clic
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11414 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011414
99.9%
116
 
0.1%

Length

2023-02-02T18:37:22.922588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:23.031262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011414
99.9%
116
 
0.1%

Most occurring characters

ValueCountFrequency (%)
011414
99.9%
116
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011414
99.9%
116
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011414
99.9%
116
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011414
99.9%
116
 
0.1%

empty_title
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
10004 
1
1426 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010004
87.5%
11426
 
12.5%

Length

2023-02-02T18:37:23.133529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:23.279917image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
010004
87.5%
11426
 
12.5%

Most occurring characters

ValueCountFrequency (%)
010004
87.5%
11426
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010004
87.5%
11426
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010004
87.5%
11426
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010004
87.5%
11426
 
12.5%

domain_in_title
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
1
8868 
0
2562 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
18868
77.6%
02562
 
22.4%

Length

2023-02-02T18:37:23.374024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:23.490904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
18868
77.6%
02562
 
22.4%

Most occurring characters

ValueCountFrequency (%)
18868
77.6%
02562
 
22.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
18868
77.6%
02562
 
22.4%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
18868
77.6%
02562
 
22.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18868
77.6%
02562
 
22.4%

domain_with_copyright
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
6406 
1
5024 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
06406
56.0%
15024
44.0%

Length

2023-02-02T18:37:23.604157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:23.719666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
06406
56.0%
15024
44.0%

Most occurring characters

ValueCountFrequency (%)
06406
56.0%
15024
44.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06406
56.0%
15024
44.0%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06406
56.0%
15024
44.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06406
56.0%
15024
44.0%

whois_registered_domain
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
10597 
1
 
833

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
010597
92.7%
1833
 
7.3%

Length

2023-02-02T18:37:23.816606image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:23.923059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
010597
92.7%
1833
 
7.3%

Most occurring characters

ValueCountFrequency (%)
010597
92.7%
1833
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
010597
92.7%
1833
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
010597
92.7%
1833
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
010597
92.7%
1833
 
7.3%

domain_registration_length
Real number (ℝ)

ZEROS

Distinct1659
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean492.532196
Minimum-1
Maximum29829
Zeros1404
Zeros (%)12.3%
Negative46
Negative (%)0.4%
Memory size89.4 KiB
2023-02-02T18:37:24.026954image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q184
median242
Q3449
95-th percentile2430
Maximum29829
Range29830
Interquartile range (IQR)365

Descriptive statistics

Standard deviation814.7694152
Coefficient of variation (CV)1.654246
Kurtosis294.6779341
Mean492.532196
Median Absolute Deviation (MAD)163
Skewness9.819607445
Sum5629643
Variance663849.1999
MonotonicityNot monotonic
2023-02-02T18:37:24.167964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01404
 
12.3%
25270
 
2.4%
374208
 
1.8%
228117
 
1.0%
21789
 
0.8%
37370
 
0.6%
37163
 
0.6%
1459
 
0.5%
90259
 
0.5%
12258
 
0.5%
Other values (1649)9033
79.0%
ValueCountFrequency (%)
-146
 
0.4%
01404
12.3%
15
 
< 0.1%
28
 
0.1%
38
 
0.1%
49
 
0.1%
57
 
0.1%
67
 
0.1%
717
 
0.1%
85
 
< 0.1%
ValueCountFrequency (%)
298291
< 0.1%
297251
< 0.1%
71021
< 0.1%
36211
< 0.1%
36111
< 0.1%
35911
< 0.1%
35711
< 0.1%
35691
< 0.1%
35681
< 0.1%
35671
< 0.1%

domain_age
Real number (ℝ)

HIGH CORRELATION

Distinct4430
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4062.543745
Minimum-12
Maximum12874
Zeros6
Zeros (%)0.1%
Negative1837
Negative (%)16.1%
Memory size89.4 KiB
2023-02-02T18:37:24.305172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-12
5-th percentile-1
Q1972.25
median3993
Q37026.75
95-th percentile8759
Maximum12874
Range12886
Interquartile range (IQR)6054.5

Descriptive statistics

Standard deviation3107.7846
Coefficient of variation (CV)0.7649848952
Kurtosis-1.097304785
Mean4062.543745
Median Absolute Deviation (MAD)3025.5
Skewness0.1641867389
Sum46434875
Variance9658325.123
MonotonicityNot monotonic
2023-02-02T18:37:24.455572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-11781
 
15.6%
7295197
 
1.7%
3993156
 
1.4%
3992117
 
1.0%
561673
 
0.6%
400370
 
0.6%
562767
 
0.6%
729667
 
0.6%
834862
 
0.5%
713360
 
0.5%
Other values (4420)8780
76.8%
ValueCountFrequency (%)
-121
 
< 0.1%
-255
 
0.5%
-11781
15.6%
06
 
0.1%
124
 
0.2%
213
 
0.1%
34
 
< 0.1%
47
 
0.1%
514
 
0.1%
63
 
< 0.1%
ValueCountFrequency (%)
128741
< 0.1%
128732
< 0.1%
128721
< 0.1%
128481
< 0.1%
128442
< 0.1%
128111
< 0.1%
128051
< 0.1%
127951
< 0.1%
127941
< 0.1%
127901
< 0.1%

web_traffic
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct4744
Distinct (%)41.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean856756.6433
Minimum0
Maximum10767986
Zeros4444
Zeros (%)38.9%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:24.596597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1651
Q3373845.5
95-th percentile5707171
Maximum10767986
Range10767986
Interquartile range (IQR)373845.5

Descriptive statistics

Standard deviation1995606.022
Coefficient of variation (CV)2.329256548
Kurtosis7.306645061
Mean856756.6433
Median Absolute Deviation (MAD)1651
Skewness2.779269268
Sum9792728433
Variance3.982443394 × 1012
MonotonicityNot monotonic
2023-02-02T18:37:24.761800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04444
38.9%
5707171176
 
1.5%
12163
 
1.4%
1148
 
1.3%
569797691
 
0.8%
581661758
 
0.5%
242
 
0.4%
838
 
0.3%
2221137
 
0.3%
436536
 
0.3%
Other values (4734)6197
54.2%
ValueCountFrequency (%)
04444
38.9%
1148
 
1.3%
242
 
0.4%
41
 
< 0.1%
838
 
0.3%
111
 
< 0.1%
12163
 
1.4%
1319
 
0.2%
154
 
< 0.1%
161
 
< 0.1%
ValueCountFrequency (%)
107679861
< 0.1%
107499991
< 0.1%
107457221
< 0.1%
107449761
< 0.1%
107252451
< 0.1%
107182271
< 0.1%
106968101
< 0.1%
106877671
< 0.1%
106842841
< 0.1%
106759271
< 0.1%

dns_record
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11200 
1
 
230

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011200
98.0%
1230
 
2.0%

Length

2023-02-02T18:37:24.914659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:25.037351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011200
98.0%
1230
 
2.0%

Most occurring characters

ValueCountFrequency (%)
011200
98.0%
1230
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011200
98.0%
1230
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011200
98.0%
1230
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011200
98.0%
1230
 
2.0%

google_index
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
1
6103 
0
5327 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
16103
53.4%
05327
46.6%

Length

2023-02-02T18:37:25.126441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:25.252434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
16103
53.4%
05327
46.6%

Most occurring characters

ValueCountFrequency (%)
16103
53.4%
05327
46.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
16103
53.4%
05327
46.6%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
16103
53.4%
05327
46.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16103
53.4%
05327
46.6%

page_rank
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.185739283
Minimum0
Maximum10
Zeros2666
Zeros (%)23.3%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:25.372377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile7
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.53695541
Coefficient of variation (CV)0.7963474675
Kurtosis-0.3863146639
Mean3.185739283
Median Absolute Deviation (MAD)2
Skewness0.4460310293
Sum36413
Variance6.436142751
MonotonicityNot monotonic
2023-02-02T18:37:25.491003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
02666
23.3%
52057
18.0%
21553
13.6%
41380
12.1%
31232
10.8%
1735
 
6.4%
6727
 
6.4%
7509
 
4.5%
10269
 
2.4%
8262
 
2.3%
ValueCountFrequency (%)
02666
23.3%
1735
 
6.4%
21553
13.6%
31232
10.8%
41380
12.1%
52057
18.0%
6727
 
6.4%
7509
 
4.5%
8262
 
2.3%
940
 
0.3%
ValueCountFrequency (%)
10269
 
2.4%
940
 
0.3%
8262
 
2.3%
7509
 
4.5%
6727
 
6.4%
52057
18.0%
41380
12.1%
31232
10.8%
21553
13.6%
1735
 
6.4%

status
Categorical

HIGH CORRELATION
UNIFORM

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
1
5715 
0
5715 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
15715
50.0%
05715
50.0%

Length

2023-02-02T18:37:25.618659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:25.726498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
15715
50.0%
05715
50.0%

Most occurring characters

ValueCountFrequency (%)
15715
50.0%
05715
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
15715
50.0%
05715
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
15715
50.0%
05715
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15715
50.0%
05715
50.0%

url_length
Real number (ℝ≥0)

HIGH CORRELATION

Distinct323
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.120035
Minimum12
Maximum1641
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:25.822848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile22
Q133
median47
Q371
95-th percentile131.55
Maximum1641
Range1629
Interquartile range (IQR)38

Descriptive statistics

Standard deviation55.29247026
Coefficient of variation (CV)0.904653773
Kurtosis144.2501023
Mean61.120035
Median Absolute Deviation (MAD)17
Skewness8.087045629
Sum698602
Variance3057.257268
MonotonicityNot monotonic
2023-02-02T18:37:25.995912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32251
 
2.2%
26251
 
2.2%
29250
 
2.2%
33231
 
2.0%
27230
 
2.0%
30227
 
2.0%
34224
 
2.0%
25221
 
1.9%
35219
 
1.9%
31219
 
1.9%
Other values (313)9107
79.7%
ValueCountFrequency (%)
121
 
< 0.1%
134
 
< 0.1%
142
 
< 0.1%
1518
 
0.2%
1620
 
0.2%
1737
 
0.3%
1856
0.5%
1986
0.8%
2084
0.7%
21131
1.1%
ValueCountFrequency (%)
16411
< 0.1%
13862
< 0.1%
9381
< 0.1%
9071
< 0.1%
7951
< 0.1%
6481
< 0.1%
6291
< 0.1%
6111
< 0.1%
5651
< 0.1%
5571
< 0.1%

https
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
6933 
1
4497 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
06933
60.7%
14497
39.3%

Length

2023-02-02T18:37:26.136859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:26.217650image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
06933
60.7%
14497
39.3%

Most occurring characters

ValueCountFrequency (%)
06933
60.7%
14497
39.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
06933
60.7%
14497
39.3%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
06933
60.7%
14497
39.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
06933
60.7%
14497
39.3%

special_characters
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size89.4 KiB
0
11430 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters11430
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
011430
100.0%

Length

2023-02-02T18:37:26.286353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2023-02-02T18:37:26.374020image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
011430
100.0%

Most occurring characters

ValueCountFrequency (%)
011430
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11430
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
011430
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common11430
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
011430
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII11430
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
011430
100.0%

digits
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct130
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.452143482
Minimum0
Maximum679
Zeros6566
Zeros (%)57.4%
Negative0
Negative (%)0.0%
Memory size89.4 KiB
2023-02-02T18:37:26.452930image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile24
Maximum679
Range679
Interquartile range (IQR)5

Descriptive statistics

Standard deviation16.31990358
Coefficient of variation (CV)2.993300458
Kurtosis315.7282022
Mean5.452143482
Median Absolute Deviation (MAD)0
Skewness12.09710155
Sum62318
Variance266.339253
MonotonicityNot monotonic
2023-02-02T18:37:26.584263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06566
57.4%
1660
 
5.8%
2483
 
4.2%
4397
 
3.5%
3379
 
3.3%
6376
 
3.3%
5284
 
2.5%
8224
 
2.0%
7187
 
1.6%
10160
 
1.4%
Other values (120)1714
 
15.0%
ValueCountFrequency (%)
06566
57.4%
1660
 
5.8%
2483
 
4.2%
3379
 
3.3%
4397
 
3.5%
5284
 
2.5%
6376
 
3.3%
7187
 
1.6%
8224
 
2.0%
9137
 
1.2%
ValueCountFrequency (%)
6791
< 0.1%
2692
< 0.1%
2671
< 0.1%
2561
< 0.1%
2331
< 0.1%
2221
< 0.1%
2201
< 0.1%
2121
< 0.1%
2111
< 0.1%
2011
< 0.1%

Interactions

2023-02-02T18:37:02.824494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:15.566019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:19.074094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:22.667974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:26.280839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:29.763231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:34.324762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:38.137446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:42.069446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:45.435633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:48.869649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:52.648254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:56.035771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:59.526556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:03.493196image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:17.756514image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:21.132618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:24.721461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:32.223837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:35.513406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:39.422673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:42.449749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:46.673579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:50.312233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:54.781248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:58.510338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:37:02.976586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:15.700705image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:19.197283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:22.778762image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:26.383345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:29.888168image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:34.447896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:38.250360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:42.164048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:45.536777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:48.979935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:52.764906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:56.159720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:59.641360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:03.618257image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:17.866374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:21.256945image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:24.829397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:32.335531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:35.616030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:39.524662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:42.586943image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:46.855763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:50.436973image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:54.912177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:58.641140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:37:03.113066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:15.814218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:19.300231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:22.893434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:26.509212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:30.015627image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:34.576327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:38.370796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:42.274459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:45.652743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:49.544283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:52.869028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:56.266670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:59.755726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:03.729934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:17.976847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:21.375754image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:24.947176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:32.452303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:35.733761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:39.595448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:42.754232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:46.997671image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:50.553525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:55.086040image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:58.762487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:37:03.261503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:15.912831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:19.405844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:23.010395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:26.606779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:30.130312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:34.700297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:38.490746image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:42.377670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:45.763006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:49.645600image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:52.980188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:56.378105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:59.858852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:03.833770image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:18.078475image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:21.498047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:25.055981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:32.568101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:35.852201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:39.667607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:42.906838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:47.131374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:50.658093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:55.253881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:58.881982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:37:03.425442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:16.031782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:19.515267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:23.124123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:26.722992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:30.255831image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:34.828809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:38.618246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:42.502379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:45.886264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:49.763441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:53.085938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:56.496776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:59.979046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:03.932863image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:18.187141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:21.624001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:25.166616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:32.680030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:35.980929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:39.749467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:43.068777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:47.250212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:50.766663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:55.410533image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:59.015332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:37:03.561891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:16.142981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:19.631611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:23.239241image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:26.831182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:30.373238image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:34.938597image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:38.749970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:42.624327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:46.006733image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:49.867370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:53.199542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:56.612244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:00.614654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:04.052878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:18.308338image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:21.748034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:25.281159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:32.782774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:36.151453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:39.872301image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:43.211202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:47.369369image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:50.877970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:55.572681image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:59.135690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:37:03.723266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:16.263986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:19.751935image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-02-02T18:36:02.871781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:06.345598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:20.568796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:24.131151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:27.508929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:34.956519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:38.746657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:42.027034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:45.786912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:49.687877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:54.063977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:57.953679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:37:02.066499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:37:06.571454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:18.603959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:22.213368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:25.844439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:29.299961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:33.786015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:37.674351image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:41.602593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:44.974787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:48.409528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:52.217908image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:55.568149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:59.052583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:02.995491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:06.535629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:20.691442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:24.241892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:27.611727image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:35.055726image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:38.835764image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:42.099779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:45.956819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:49.790840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:54.178876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:58.065478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:37:02.223183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:37:06.705392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:18.716845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:22.327557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:25.960352image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:29.404364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:33.960666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:37.782951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:41.716632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:45.089142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:48.514280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:52.327560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:55.686646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:59.173645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:03.107856image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:06.643358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:20.802388image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:24.363735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:27.731903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:35.165960image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:38.933618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:42.192981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:46.128687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:49.943704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:54.295269image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:58.175867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:37:02.372470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:37:06.836272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:18.835824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:22.440099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:26.064008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:29.522036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:34.092821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:37.893474image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:41.821758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:45.203752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:48.626889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:52.427710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:55.789968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:59.287922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:03.226409image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:06.750696image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:20.910275image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:24.482849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:27.835821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:35.277034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:39.100432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:42.283635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:46.291568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:50.099682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:54.445291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:58.269887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:37:02.525348image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:37:07.002598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:18.952812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:22.557685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:26.172870image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:29.635797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:34.204838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:38.014311image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:41.943451image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:45.317806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:48.751344image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:52.540177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:55.907967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:35:59.411308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:03.366425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:06.860550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:21.021714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:24.600508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:27.947791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:35.396857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:39.256865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:42.359790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:46.485436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:50.210615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:54.642128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:36:58.378914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-02-02T18:37:02.685753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-02-02T18:37:26.787580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-02-02T18:37:28.198923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-02-02T18:37:29.581075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-02-02T18:37:30.738101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2023-02-02T18:37:31.115832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-02-02T18:37:07.570088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

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3http://rgipt.ac.in00000000002000010105505505.0000005.00.0000000000001490.9731540.0268460000.25000000.25000000100.000000096.4285713.57142900062.50000000010062-1107721003118000
4http://www.iracing.com/tracks/gateway-motorsports-park/0100000000200001063334117116.3333335.07.0000000000001020.4705880.5294120000.53703700.0185191076.47058800.000000100.0000000000.00000000001022481758725006155000
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8http://vamoaestudiarmedicina.blogspot.com/00000000002001000208802121014.50000014.50.000000010000630.2063490.7936510300.38000000.000000010.00000000.000000100.00000000027.27272700011037172980005142000
9https://parade.com/425836/joshwigler/the-amazing-race-host-phil-keoghan-previews-the-season-27-premiere/00000000001000000140262106105.5714296.05.5384621000001400.7785710.2214290100.19354800.0000000193.103448010.00000090.00000000058.139535000100128936867740051104108

Last rows

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11420https://adnanboz.wordpress.com/2012/01/06/how-to-set-up-amazon-ec2-windows-gpu-instance-for-nvidia-cuda-development/00000000002000000181282119114.7777788.504.312500010000160.1875000.8125000400.00000000.0000000116.66666700.0000000.0000000000.000000000100585744900081116109
11421http://www.peoplemakingplaces.com/includes/Support/En/log/signin/customer_center/customer-IDPP00C644/myaccount/signin010000000021000101382321818107.23076910.506.636364400000420.8095240.1904760100.50000000.0000000150.0000000100.0000000.00000000020.000000000100134205800120117005
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11423http://www.dmega.co.kr/dmega/data/qna/sec/page.php?email=ZmFpdGhAc2VtYW50aWMuaW5mbw==01000000003000000103333265266.1000004.006.625000000000921.0000000.0000000000.00000000.00000000100.0000000100.0000000.000000010100.000000000010293518610408014011085004
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11427https://www.facebook.com/Interactive-Television-Pvt-Ltd-Group-M-100230523435650/photos/?ref=page_internal11000000002000000135131158156.1538465.506.272727010000680.4705880.5294120500.00000000.000000016.25000000.0000000.00000000080.000000000000280985158011011051015
11428http://www.mypublicdomainpictures.com/01000000002000000233302222012.50000012.500.000000000000320.3750000.6250000100.05000000.0500000116.66666700.000000100.0000000000.0000000001008528362455493004138000
11429http://174.139.46.123/ap/signin?openid.pape.max_auth_age=0&amp;openid.return_to=https%3A%2F%2Fwww.amazon.co.jp%2F%3Fref_%3Dnav_em_hd_re_signin&amp;openid.identity=http%3A%2F%2Fspecs.openid.net%2Fauth%2F2.0%2Fidentifier_select&amp;openid.assoc_handle=jpflex&amp;openid.mode=checkid_setup&amp;key=a@b.c&amp;openid.claimed_id=http%3A%2F%2Fspecs.openid.net%2Fauth%2F2.0%2Fidentifier_select&amp;openid.ns=http%3A%2F%2Fspecs.openid.net%2Fauth%2F2.0&amp;&amp;ref_=nav_em_hd_clc_signin11004001113000011908121123124.3777782.754.453488300102210.4285710.5714290300.00000000.083333110.00000000.0000000.00000000033.3333330001110-1011004771041